Overview

Dataset statistics

Number of variables19
Number of observations103
Missing cells208
Missing cells (%)10.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory153.2 B

Variable types

Numeric12
DateTime1
Categorical4
Unsupported2

Warnings

Channel has constant value "Reclame Aqui" Constant
Tickets has constant value "1" Constant
Requester email has a high cardinality: 103 distinct values High cardinality
df_index is highly correlated with Requester external ID and 1 other fieldsHigh correlation
Requester external ID is highly correlated with df_index and 1 other fieldsHigh correlation
Requester_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with Volume_CHATHigh correlation
df_index is highly correlated with Requester external ID and 1 other fieldsHigh correlation
Requester external ID is highly correlated with df_index and 1 other fieldsHigh correlation
Requester_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with CSAT_RatedHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with Volume_CHAT and 1 other fieldsHigh correlation
df_index is highly correlated with Requester external ID and 1 other fieldsHigh correlation
Requester external ID is highly correlated with df_index and 1 other fieldsHigh correlation
Requester_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with Volume_CHATHigh correlation
Requester_ID is highly correlated with Requester external ID and 2 other fieldsHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_Rated and 1 other fieldsHigh correlation
Ticket ID is highly correlated with Assignee email and 1 other fieldsHigh correlation
%NFCR is highly correlated with Volume_CHAT and 1 other fieldsHigh correlation
Volume_EMAIL is highly correlated with Tempo_Medio_EmailHigh correlation
Requester external ID is highly correlated with Requester_ID and 2 other fieldsHigh correlation
AWT_Chat is highly correlated with Ticket created - DateHigh correlation
Tempo_Medio_Chat is highly correlated with Ticket created - DateHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT) and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with %Insatisfação(CSAT) and 3 other fieldsHigh correlation
Assignee email is highly correlated with Ticket IDHigh correlation
Tempo_Medio_Email is highly correlated with Volume_EMAILHigh correlation
df_index is highly correlated with Requester_ID and 2 other fieldsHigh correlation
Ticket created - Date is highly correlated with Requester_ID and 7 other fieldsHigh correlation
Channel is highly correlated with Tickets and 1 other fieldsHigh correlation
Tickets is highly correlated with Channel and 1 other fieldsHigh correlation
Assignee email is highly correlated with Channel and 1 other fieldsHigh correlation
Volume_SOCIAL has 103 (100.0%) missing values Missing
Tempo_Medio_Social has 103 (100.0%) missing values Missing
Requester email is uniformly distributed Uniform
df_index has unique values Unique
Requester external ID has unique values Unique
Ticket ID has unique values Unique
Requester email has unique values Unique
Requester_ID has unique values Unique
Volume_SOCIAL is an unsupported type, check if it needs cleaning or further analysis Unsupported
Tempo_Medio_Social is an unsupported type, check if it needs cleaning or further analysis Unsupported
%NFCR has 28 (27.2%) zeros Zeros
%Insatisfação(CSAT) has 66 (64.1%) zeros Zeros
CSAT_Rated has 34 (33.0%) zeros Zeros

Reproduction

Analysis started2021-06-15 16:42:15.780710
Analysis finished2021-06-15 16:42:41.627107
Duration25.85 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489.6504854
Minimum4
Maximum932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:41.726841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile54.1
Q1279
median487
Q3720
95-th percentile904.3
Maximum932
Range928
Interquartile range (IQR)441

Descriptive statistics

Standard deviation268.4522344
Coefficient of variation (CV)0.5482527687
Kurtosis-1.152105309
Mean489.6504854
Median Absolute Deviation (MAD)224
Skewness-0.04097715684
Sum50434
Variance72066.60213
MonotonicityStrictly increasing
2021-06-15T13:42:41.895524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5101
 
1.0%
6111
 
1.0%
3301
 
1.0%
3351
 
1.0%
5961
 
1.0%
851
 
1.0%
5981
 
1.0%
3821
 
1.0%
7311
 
1.0%
8631
 
1.0%
Other values (93)93
90.3%
ValueCountFrequency (%)
41
1.0%
181
1.0%
241
1.0%
331
1.0%
401
1.0%
521
1.0%
731
1.0%
851
1.0%
1071
1.0%
1281
1.0%
ValueCountFrequency (%)
9321
1.0%
9261
1.0%
9241
1.0%
9231
1.0%
9151
1.0%
9061
1.0%
8891
1.0%
8771
1.0%
8691
1.0%
8631
1.0%

Requester external ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8914559.019
Minimum54194
Maximum19066870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:42.046988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum54194
5-th percentile506556.6
Q14467439.5
median8220013
Q313809182.5
95-th percentile17307331.9
Maximum19066870
Range19012676
Interquartile range (IQR)9341743

Descriptive statistics

Standard deviation5635688.525
Coefficient of variation (CV)0.6321892662
Kurtosis-1.209150668
Mean8914559.019
Median Absolute Deviation (MAD)4964030
Skewness0.120628466
Sum918199579
Variance3.176098516 × 1013
MonotonicityStrictly increasing
2021-06-15T13:42:42.202569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8534581
 
1.0%
110729111
 
1.0%
68060221
 
1.0%
98232901
 
1.0%
166267911
 
1.0%
65521511
 
1.0%
4045181
 
1.0%
138875001
 
1.0%
87545041
 
1.0%
58600141
 
1.0%
Other values (93)93
90.3%
ValueCountFrequency (%)
541941
1.0%
1488751
1.0%
2159271
1.0%
3487181
1.0%
4045181
1.0%
4883351
1.0%
6705511
1.0%
8534581
1.0%
10094001
1.0%
11864031
1.0%
ValueCountFrequency (%)
190668701
1.0%
188144671
1.0%
186857711
1.0%
186851621
1.0%
185702941
1.0%
173261761
1.0%
171377351
1.0%
170504261
1.0%
169413421
1.0%
169208281
1.0%

Ticket created - Date
Date

HIGH CORRELATION

Distinct70
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
Minimum2021-01-07 00:00:00
Maximum2021-06-09 00:00:00
2021-06-15T13:42:42.405514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:42.666239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Channel
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
Reclame Aqui
103 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1236
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReclame Aqui
2nd rowReclame Aqui
3rd rowReclame Aqui
4th rowReclame Aqui
5th rowReclame Aqui

Common Values

ValueCountFrequency (%)
Reclame Aqui103
100.0%

Length

2021-06-15T13:42:43.176665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T13:42:43.245419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
reclame103
50.0%
aqui103
50.0%

Most occurring characters

ValueCountFrequency (%)
e206
16.7%
R103
8.3%
c103
8.3%
l103
8.3%
a103
8.3%
m103
8.3%
103
8.3%
A103
8.3%
q103
8.3%
u103
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter927
75.0%
Uppercase Letter206
 
16.7%
Space Separator103
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e206
22.2%
c103
11.1%
l103
11.1%
a103
11.1%
m103
11.1%
q103
11.1%
u103
11.1%
i103
11.1%
Uppercase Letter
ValueCountFrequency (%)
R103
50.0%
A103
50.0%
Space Separator
ValueCountFrequency (%)
103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1133
91.7%
Common103
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e206
18.2%
R103
9.1%
c103
9.1%
l103
9.1%
a103
9.1%
m103
9.1%
A103
9.1%
q103
9.1%
u103
9.1%
i103
9.1%
Common
ValueCountFrequency (%)
103
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e206
16.7%
R103
8.3%
c103
8.3%
l103
8.3%
a103
8.3%
m103
8.3%
103
8.3%
A103
8.3%
q103
8.3%
u103
8.3%

Ticket ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3788058.282
Minimum3489502
Maximum4078417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:43.326940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3489502
5-th percentile3521645.3
Q13615755
median3795980
Q33954945.5
95-th percentile4024304.4
Maximum4078417
Range588915
Interquartile range (IQR)339190.5

Descriptive statistics

Standard deviation179469.1107
Coefficient of variation (CV)0.04737760017
Kurtosis-1.452945056
Mean3788058.282
Median Absolute Deviation (MAD)167224
Skewness-0.1176438073
Sum390170003
Variance3.220916169 × 1010
MonotonicityNot monotonic
2021-06-15T13:42:43.445924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35962861
 
1.0%
37520211
 
1.0%
39534721
 
1.0%
39632041
 
1.0%
37947621
 
1.0%
37589241
 
1.0%
39778051
 
1.0%
39760131
 
1.0%
35162371
 
1.0%
39652641
 
1.0%
Other values (93)93
90.3%
ValueCountFrequency (%)
34895021
1.0%
34972351
1.0%
35054331
1.0%
35162371
1.0%
35171901
1.0%
35211531
1.0%
35260761
1.0%
35274421
1.0%
35371661
1.0%
35371961
1.0%
ValueCountFrequency (%)
40784171
1.0%
40715841
1.0%
40696211
1.0%
40623411
1.0%
40304691
1.0%
40251101
1.0%
40170541
1.0%
40167621
1.0%
40125081
1.0%
40115701
1.0%

Assignee email
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size952.0 B
kaique.barbosa@gympass.com
32 
barbara.priscila@gympass.com
16 
suellen.franco+core@gympass.com
15 
danielle.hernandes@gympass.com
14 
luciana.melo@gympass.com
10 
Other values (7)
16 

Length

Max length31
Median length27
Mean length27.52427184
Min length23

Characters and Unicode

Total characters2835
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.9%

Sample

1st rowgabriel.almeida@gympass.com
2nd rowbarbara.priscila@gympass.com
3rd rowkaique.barbosa@gympass.com
4th rowluciana.melo@gympass.com
5th rowsuellen.franco+core@gympass.com

Common Values

ValueCountFrequency (%)
kaique.barbosa@gympass.com32
31.1%
barbara.priscila@gympass.com16
15.5%
suellen.franco+core@gympass.com15
14.6%
danielle.hernandes@gympass.com14
13.6%
luciana.melo@gympass.com10
 
9.7%
anderson.santos@gympass.com5
 
4.9%
gabriel.almeida@gympass.com3
 
2.9%
elizabete.damiao@gympass.com3
 
2.9%
caroline.roberto@gympass.com2
 
1.9%
yasmin.reis@gympass.com1
 
1.0%
Other values (2)2
 
1.9%

Length

2021-06-15T13:42:43.744615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kaique.barbosa@gympass.com32
31.1%
barbara.priscila@gympass.com16
15.5%
suellen.franco+core@gympass.com15
14.6%
danielle.hernandes@gympass.com14
13.6%
luciana.melo@gympass.com10
 
9.7%
anderson.santos@gympass.com5
 
4.9%
gabriel.almeida@gympass.com3
 
2.9%
elizabete.damiao@gympass.com3
 
2.9%
caroline.roberto@gympass.com2
 
1.9%
yasmin.reis@gympass.com1
 
1.0%
Other values (2)2
 
1.9%

Most occurring characters

ValueCountFrequency (%)
a359
12.7%
s301
 
10.6%
m224
 
7.9%
.206
 
7.3%
o199
 
7.0%
e171
 
6.0%
c163
 
5.7%
r141
 
5.0%
p119
 
4.2%
l108
 
3.8%
Other values (15)844
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2511
88.6%
Other Punctuation309
 
10.9%
Math Symbol15
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a359
14.3%
s301
12.0%
m224
 
8.9%
o199
 
7.9%
e171
 
6.8%
c163
 
6.5%
r141
 
5.6%
p119
 
4.7%
l108
 
4.3%
g106
 
4.2%
Other values (12)620
24.7%
Other Punctuation
ValueCountFrequency (%)
.206
66.7%
@103
33.3%
Math Symbol
ValueCountFrequency (%)
+15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2511
88.6%
Common324
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a359
14.3%
s301
12.0%
m224
 
8.9%
o199
 
7.9%
e171
 
6.8%
c163
 
6.5%
r141
 
5.6%
p119
 
4.7%
l108
 
4.3%
g106
 
4.2%
Other values (12)620
24.7%
Common
ValueCountFrequency (%)
.206
63.6%
@103
31.8%
+15
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a359
12.7%
s301
 
10.6%
m224
 
7.9%
.206
 
7.3%
o199
 
7.0%
e171
 
6.0%
c163
 
5.7%
r141
 
5.0%
p119
 
4.2%
l108
 
3.8%
Other values (15)844
29.8%

Requester email
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
camilampm@yahoo.com.br
 
1
alexandropersi@gmail.com
 
1
thais.arruda.silva@gmail.com
 
1
giovana.sanini@buser.com.br
 
1
wander@fasa.edu.br
 
1
Other values (98)
98 

Length

Max length62
Median length25
Mean length25.21359223
Min length16

Characters and Unicode

Total characters2597
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)100.0%

Sample

1st rownancy.jikihara@voith.com
2nd rowwesbley.carvalho@livup.com.br
3rd rowlaura.andrade@erm.com
4th rowguilhermequintino@gmail.com
5th rowjoice.bertechini@gmail.com

Common Values

ValueCountFrequency (%)
camilampm@yahoo.com.br1
 
1.0%
alexandropersi@gmail.com1
 
1.0%
thais.arruda.silva@gmail.com1
 
1.0%
giovana.sanini@buser.com.br1
 
1.0%
wander@fasa.edu.br1
 
1.0%
na.matiolli@gmail.com1
 
1.0%
guilhermemoura403@gmail.com1
 
1.0%
ricardodegoescorreia@gmail.com1
 
1.0%
smtreinamentofuncional@gmail.com1
 
1.0%
sergiotgbarros@uol.com.br1
 
1.0%
Other values (93)93
90.3%

Length

2021-06-15T13:42:44.048119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
camilampm@yahoo.com.br1
 
1.0%
alexandropersi@gmail.com1
 
1.0%
thais.arruda.silva@gmail.com1
 
1.0%
giovana.sanini@buser.com.br1
 
1.0%
wander@fasa.edu.br1
 
1.0%
na.matiolli@gmail.com1
 
1.0%
guilhermemoura403@gmail.com1
 
1.0%
ricardodegoescorreia@gmail.com1
 
1.0%
smtreinamentofuncional@gmail.com1
 
1.0%
sergiotgbarros@uol.com.br1
 
1.0%
Other values (93)93
90.3%

Most occurring characters

ValueCountFrequency (%)
a287
 
11.1%
o262
 
10.1%
m216
 
8.3%
i179
 
6.9%
l166
 
6.4%
.163
 
6.3%
c152
 
5.9%
r138
 
5.3%
e127
 
4.9%
@103
 
4.0%
Other values (30)804
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2223
85.6%
Other Punctuation266
 
10.2%
Decimal Number85
 
3.3%
Dash Punctuation14
 
0.5%
Connector Punctuation9
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a287
12.9%
o262
11.8%
m216
9.7%
i179
 
8.1%
l166
 
7.5%
c152
 
6.8%
r138
 
6.2%
e127
 
5.7%
s95
 
4.3%
n89
 
4.0%
Other values (16)512
23.0%
Decimal Number
ValueCountFrequency (%)
114
16.5%
013
15.3%
610
11.8%
210
11.8%
89
10.6%
57
8.2%
97
8.2%
36
7.1%
45
 
5.9%
74
 
4.7%
Other Punctuation
ValueCountFrequency (%)
.163
61.3%
@103
38.7%
Dash Punctuation
ValueCountFrequency (%)
-14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2223
85.6%
Common374
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a287
12.9%
o262
11.8%
m216
9.7%
i179
 
8.1%
l166
 
7.5%
c152
 
6.8%
r138
 
6.2%
e127
 
5.7%
s95
 
4.3%
n89
 
4.0%
Other values (16)512
23.0%
Common
ValueCountFrequency (%)
.163
43.6%
@103
27.5%
-14
 
3.7%
114
 
3.7%
013
 
3.5%
610
 
2.7%
210
 
2.7%
_9
 
2.4%
89
 
2.4%
57
 
1.9%
Other values (4)22
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a287
 
11.1%
o262
 
10.1%
m216
 
8.3%
i179
 
6.9%
l166
 
6.4%
.163
 
6.3%
c152
 
5.9%
r138
 
5.3%
e127
 
4.9%
@103
 
4.0%
Other values (30)804
31.0%

Tickets
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
1
103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1103
100.0%

Length

2021-06-15T13:42:44.271240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T13:42:44.358627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1103
100.0%

Most occurring characters

ValueCountFrequency (%)
1103
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number103
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1103
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1103
100.0%

Requester_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8914559.019
Minimum54194
Maximum19066870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:44.439445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum54194
5-th percentile506556.6
Q14467439.5
median8220013
Q313809182.5
95-th percentile17307331.9
Maximum19066870
Range19012676
Interquartile range (IQR)9341743

Descriptive statistics

Standard deviation5635688.525
Coefficient of variation (CV)0.6321892662
Kurtosis-1.209150668
Mean8914559.019
Median Absolute Deviation (MAD)4964030
Skewness0.120628466
Sum918199579
Variance3.176098516 × 1013
MonotonicityStrictly increasing
2021-06-15T13:42:44.589012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169208281
 
1.0%
173261761
 
1.0%
171377351
 
1.0%
87545041
 
1.0%
133590561
 
1.0%
53998851
 
1.0%
59897121
 
1.0%
33508691
 
1.0%
65521511
 
1.0%
87775601
 
1.0%
Other values (93)93
90.3%
ValueCountFrequency (%)
541941
1.0%
1488751
1.0%
2159271
1.0%
3487181
1.0%
4045181
1.0%
4883351
1.0%
6705511
1.0%
8534581
1.0%
10094001
1.0%
11864031
1.0%
ValueCountFrequency (%)
190668701
1.0%
188144671
1.0%
186857711
1.0%
186851621
1.0%
185702941
1.0%
173261761
1.0%
171377351
1.0%
170504261
1.0%
169413421
1.0%
169208281
1.0%

Volume_CHAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.650485437
Minimum0
Maximum16
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:44.728487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile7.9
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.371020868
Coefficient of variation (CV)0.894560987
Kurtosis9.856650226
Mean2.650485437
Median Absolute Deviation (MAD)1
Skewness2.592026464
Sum273
Variance5.621739958
MonotonicityNot monotonic
2021-06-15T13:42:44.823263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
144
42.7%
319
18.4%
215
 
14.6%
410
 
9.7%
56
 
5.8%
93
 
2.9%
82
 
1.9%
01
 
1.0%
71
 
1.0%
161
 
1.0%
ValueCountFrequency (%)
01
 
1.0%
144
42.7%
215
 
14.6%
319
18.4%
410
 
9.7%
56
 
5.8%
61
 
1.0%
71
 
1.0%
82
 
1.9%
93
 
2.9%
ValueCountFrequency (%)
161
 
1.0%
93
 
2.9%
82
 
1.9%
71
 
1.0%
61
 
1.0%
56
 
5.8%
410
 
9.7%
319
18.4%
215
 
14.6%
144
42.7%

Volume_EMAIL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.563106796
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:44.929424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.025841624
Coefficient of variation (CV)0.6562837719
Kurtosis15.39185153
Mean1.563106796
Median Absolute Deviation (MAD)0
Skewness3.244045751
Sum161
Variance1.052351038
MonotonicityNot monotonic
2021-06-15T13:42:45.019183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
166
64.1%
225
 
24.3%
38
 
7.8%
42
 
1.9%
51
 
1.0%
81
 
1.0%
ValueCountFrequency (%)
166
64.1%
225
 
24.3%
38
 
7.8%
42
 
1.9%
51
 
1.0%
81
 
1.0%
ValueCountFrequency (%)
81
 
1.0%
51
 
1.0%
42
 
1.9%
38
 
7.8%
225
 
24.3%
166
64.1%

Volume_SOCIAL
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing103
Missing (%)100.0%
Memory size952.0 B

Tempo_Medio_Chat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)99.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1376.588235
Minimum15
Maximum3114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:45.146812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile418.9
Q1918.25
median1323
Q31716.25
95-th percentile2680.75
Maximum3114
Range3099
Interquartile range (IQR)798

Descriptive statistics

Standard deviation650.0578398
Coefficient of variation (CV)0.4722238816
Kurtosis0.05939509507
Mean1376.588235
Median Absolute Deviation (MAD)397
Skewness0.5083057296
Sum140412
Variance422575.1951
MonotonicityNot monotonic
2021-06-15T13:42:45.277493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19522
 
1.9%
21431
 
1.0%
7261
 
1.0%
13601
 
1.0%
4361
 
1.0%
15121
 
1.0%
571
 
1.0%
8781
 
1.0%
8161
 
1.0%
26811
 
1.0%
Other values (91)91
88.3%
ValueCountFrequency (%)
151
1.0%
571
1.0%
3771
1.0%
3841
1.0%
4131
1.0%
4181
1.0%
4361
1.0%
5521
1.0%
5531
1.0%
5551
1.0%
ValueCountFrequency (%)
31141
1.0%
28991
1.0%
27691
1.0%
27221
1.0%
26891
1.0%
26811
1.0%
26761
1.0%
26071
1.0%
25461
1.0%
24401
1.0%

Tempo_Medio_Email
Real number (ℝ≥0)

HIGH CORRELATION

Distinct102
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11334.81553
Minimum1470
Maximum57865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:45.422075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1470
5-th percentile2669.1
Q15254
median8286
Q314934
95-th percentile27134.9
Maximum57865
Range56395
Interquartile range (IQR)9680

Descriptive statistics

Standard deviation9048.550857
Coefficient of variation (CV)0.7982971429
Kurtosis6.65449322
Mean11334.81553
Median Absolute Deviation (MAD)4375
Skewness2.097650557
Sum1167486
Variance81876272.6
MonotonicityNot monotonic
2021-06-15T13:42:45.551486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158432
 
1.9%
175431
 
1.0%
223961
 
1.0%
68201
 
1.0%
117801
 
1.0%
32381
 
1.0%
82861
 
1.0%
34421
 
1.0%
189041
 
1.0%
106251
 
1.0%
Other values (92)92
89.3%
ValueCountFrequency (%)
14701
1.0%
19321
1.0%
21831
1.0%
22441
1.0%
25691
1.0%
26401
1.0%
29311
1.0%
30611
1.0%
31211
1.0%
32071
1.0%
ValueCountFrequency (%)
578651
1.0%
385201
1.0%
347281
1.0%
325391
1.0%
320151
1.0%
271911
1.0%
266301
1.0%
238321
1.0%
227331
1.0%
227121
1.0%

Tempo_Medio_Social
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing103
Missing (%)100.0%
Memory size952.0 B

AWT_Chat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)64.7%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean152.0098039
Minimum7
Maximum1981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:45.701833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8
Q118.5
median37.5
Q3113.5
95-th percentile680.95
Maximum1981
Range1974
Interquartile range (IQR)95

Descriptive statistics

Standard deviation301.0175974
Coefficient of variation (CV)1.980251205
Kurtosis19.32820308
Mean152.0098039
Median Absolute Deviation (MAD)26.5
Skewness4.015252905
Sum15505
Variance90611.59396
MonotonicityNot monotonic
2021-06-15T13:42:45.851440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75
 
4.9%
335
 
4.9%
114
 
3.9%
244
 
3.9%
83
 
2.9%
133
 
2.9%
353
 
2.9%
233
 
2.9%
143
 
2.9%
1152
 
1.9%
Other values (56)67
65.0%
ValueCountFrequency (%)
75
4.9%
83
2.9%
91
 
1.0%
114
3.9%
122
 
1.9%
133
2.9%
143
2.9%
162
 
1.9%
172
 
1.9%
181
 
1.0%
ValueCountFrequency (%)
19811
1.0%
16911
1.0%
8431
1.0%
8011
1.0%
7651
1.0%
6891
1.0%
5281
1.0%
5171
1.0%
4791
1.0%
4291
1.0%

%NFCR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3170832705
Minimum0
Maximum1
Zeros28
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:45.995443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.3333333333
Q30.5
95-th percentile0.6666666667
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2370517885
Coefficient of variation (CV)0.747601058
Kurtosis-0.7273449227
Mean0.3170832705
Median Absolute Deviation (MAD)0.1666666667
Skewness0.0564166741
Sum32.65957686
Variance0.05619355043
MonotonicityNot monotonic
2021-06-15T13:42:46.134676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
028
27.2%
0.525
24.3%
0.333333333313
12.6%
0.258
 
7.8%
0.45
 
4.9%
0.24
 
3.9%
0.66666666674
 
3.9%
0.16666666672
 
1.9%
0.62
 
1.9%
0.32
 
1.9%
Other values (9)10
 
9.7%
ValueCountFrequency (%)
028
27.2%
0.16666666672
 
1.9%
0.24
 
3.9%
0.258
 
7.8%
0.32
 
1.9%
0.333333333313
12.6%
0.3751
 
1.0%
0.45
 
4.9%
0.42857142861
 
1.0%
0.44444444441
 
1.0%
ValueCountFrequency (%)
11
 
1.0%
0.752
 
1.9%
0.71428571431
 
1.0%
0.66666666674
 
3.9%
0.64705882351
 
1.0%
0.62
 
1.9%
0.57142857141
 
1.0%
0.54545454551
 
1.0%
0.525
24.3%
0.44444444441
 
1.0%

%Insatisfação(CSAT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2897311426
Minimum0
Maximum1
Zeros66
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:46.256357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4184600572
Coefficient of variation (CV)1.444304721
Kurtosis-0.9258565024
Mean0.2897311426
Median Absolute Deviation (MAD)0
Skewness0.9332667072
Sum29.84230769
Variance0.1751088195
MonotonicityNot monotonic
2021-06-15T13:42:46.368066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
066
64.1%
123
 
22.3%
0.59
 
8.7%
0.33333333331
 
1.0%
0.41
 
1.0%
0.69230769231
 
1.0%
0.66666666671
 
1.0%
0.251
 
1.0%
ValueCountFrequency (%)
066
64.1%
0.251
 
1.0%
0.33333333331
 
1.0%
0.41
 
1.0%
0.59
 
8.7%
0.66666666671
 
1.0%
0.69230769231
 
1.0%
123
 
22.3%
ValueCountFrequency (%)
123
 
22.3%
0.69230769231
 
1.0%
0.66666666671
 
1.0%
0.59
 
8.7%
0.41
 
1.0%
0.33333333331
 
1.0%
0.251
 
1.0%
066
64.1%

CSAT_Rated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.203883495
Minimum0
Maximum13
Zeros34
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2021-06-15T13:42:46.470783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.62914162
Coefficient of variation (CV)1.353238604
Kurtosis27.12441364
Mean1.203883495
Median Absolute Deviation (MAD)1
Skewness4.269709731
Sum124
Variance2.654102418
MonotonicityNot monotonic
2021-06-15T13:42:46.586476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
139
37.9%
034
33.0%
223
22.3%
52
 
1.9%
32
 
1.9%
131
 
1.0%
61
 
1.0%
41
 
1.0%
ValueCountFrequency (%)
034
33.0%
139
37.9%
223
22.3%
32
 
1.9%
41
 
1.0%
52
 
1.9%
61
 
1.0%
131
 
1.0%
ValueCountFrequency (%)
131
 
1.0%
61
 
1.0%
52
 
1.9%
41
 
1.0%
32
 
1.9%
223
22.3%
139
37.9%
034
33.0%

Interactions

2021-06-15T13:42:16.788931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:17.025192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:17.307449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:17.498940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:17.732314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:17.972197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:18.139589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:18.295291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:18.468602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:18.634191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:18.791839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:18.940376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:19.089490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:19.259664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:19.462393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:19.645626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:19.846689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:20.038178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:20.227858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:20.426839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:20.629328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:20.823776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:20.993881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:21.169093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:21.344623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:21.490205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:21.723742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:21.951161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:22.141064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:22.391366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:22.647345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:22.913633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:23.668765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:23.855294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:24.013903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:24.185982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:24.358624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:24.528171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:24.717664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:24.897184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:25.093553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:25.303027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:25.490825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:25.672009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:25.929354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:26.112828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:26.274396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:26.430159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:26.574773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:26.731965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:26.915883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.082992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.256222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.407295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.534351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.672176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.812430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:27.944618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.065396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.200091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.331953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.430283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.577695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.703949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.840163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:28.960582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.087225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.327863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.462355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.576833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.700197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.820846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:29.946140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.046968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.178034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.294865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.441601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.562335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.678726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.794678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:30.911746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.044491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.158692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.277766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.395295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.513845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.645340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.778203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:31.917545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.050880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.163038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.297522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.412203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.545481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.645456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.779302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:32.907985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:33.047584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:33.227141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:33.379770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:33.544225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:33.695477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:33.824289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:34.090612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:34.312219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:34.537980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:34.800278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:34.990768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:35.203570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:35.378224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:35.592541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:35.900775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:36.069601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:36.273689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:36.434261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:36.580899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:36.734458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:36.914998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.051483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.176147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.298039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.423671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.578768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.710630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:37.879221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.023065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.153682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.290346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.424955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.557600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.671328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.788015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:38.910168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.027878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.163523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.288190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.424042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.548709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.671481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.792121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:39.922797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:40.070351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:40.197978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:40.355613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-15T13:42:46.713181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-15T13:42:47.022503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-15T13:42:47.315793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-15T13:42:47.601030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-15T13:42:47.832412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-15T13:42:40.631177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-15T13:42:41.090647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-15T13:42:41.326668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-15T13:42:41.445889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexRequester external IDTicket created - DateChannelTicket IDAssignee emailRequester emailTicketsRequester_IDVolume_CHATVolume_EMAILVolume_SOCIALTempo_Medio_ChatTempo_Medio_EmailTempo_Medio_SocialAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_Rated
0454194.02021-03-22Reclame Aqui3795980gabriel.almeida@gympass.comnancy.jikihara@voith.com1541941.01.0NaN1066.057865.0NaN169.00.5000001.01
118148875.02021-05-07Reclame Aqui3956419barbara.priscila@gympass.comwesbley.carvalho@livup.com.br11488756.01.0NaN1309.023832.0NaN138.00.7142861.01
224215927.02021-01-26Reclame Aqui3568930kaique.barbosa@gympass.comlaura.andrade@erm.com12159273.01.0NaN1884.01932.0NaN14.00.2500001.01
333348718.02021-05-04Reclame Aqui3943532luciana.melo@gympass.comguilhermequintino@gmail.com13487181.01.0NaN1688.032539.0NaN89.00.0000001.01
440404518.02021-02-09Reclame Aqui3629138suellen.franco+core@gympass.comjoice.bertechini@gmail.com14045183.01.0NaN1728.010625.0NaN87.00.2500000.02
552488335.02021-01-27Reclame Aqui3574448danielle.hernandes@gympass.comvanessa-fcampos@hotmail.com14883352.01.0NaN1224.011389.0NaN33.00.6666671.02
673670551.02021-02-01Reclame Aqui3589849kaique.barbosa@gympass.comluba_balbal@hotmail.com16705513.01.0NaN1952.05178.0NaN11.00.0000000.00
785853458.02021-05-26Reclame Aqui4025110kaique.barbosa@gympass.comsergiotgbarros@uol.com.br18534581.08.0NaN1477.010458.0NaN7.00.7500000.00
81071009400.02021-05-11Reclame Aqui3969476barbara.priscila@gympass.combmenndes@gmail.com110094002.03.0NaN1324.06792.0NaN35.00.3333330.00
91281186403.02021-05-05Reclame Aqui3947655barbara.priscila@gympass.comluanycardoso@gmail.com111864033.01.0NaN1322.03121.0NaN11.00.2500000.02

Last rows

df_indexRequester external IDTicket created - DateChannelTicket IDAssignee emailRequester emailTicketsRequester_IDVolume_CHATVolume_EMAILVolume_SOCIALTempo_Medio_ChatTempo_Medio_EmailTempo_Medio_SocialAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_Rated
9386316920828.02021-01-22Reclame Aqui3557795kaique.barbosa@gympass.comcaroline.bonesi@gmail.com1169208281.02.0NaN961.07892.0NaN13.00.3333331.001
9486916941342.02021-04-30Reclame Aqui3929623kaique.barbosa@gympass.comex.ravem666@gmail.com1169413423.03.0NaN821.011219.0NaN72.00.5000001.002
9587717050426.02021-05-10Reclame Aqui3963204kaique.barbosa@gympass.combina131@live.com1170504261.01.0NaN665.012316.0NaN109.00.0000001.001
9688917137735.02021-05-24Reclame Aqui4011570danielle.hernandes@gympass.commauricio.miranda@stone.com.br1171377354.01.0NaN1754.07338.0NaN23.00.4000001.002
9790617326176.02021-04-12Reclame Aqui3865534danielle.hernandes@gympass.comcristiano.body@gmail.com1173261761.01.0NaN418.03553.0NaN46.00.0000000.001
9891518570294.02021-04-20Reclame Aqui3892841danielle.hernandes@gympass.comfabionunesp@hotmail.com1185702942.04.0NaN2769.038520.0NaN11.00.5000000.001
9992318685162.02021-05-19Reclame Aqui3996381barbara.priscila@gympass.comthales.berdu@bayer.com1186851621.02.0NaN1013.012767.0NaN1691.00.5000001.001
10092418685771.02021-05-18Reclame Aqui3993205kaique.barbosa@gympass.comandre.martins@modal.com.br1186857715.01.0NaN962.011879.0NaN332.00.5000000.254
10192618814467.02021-05-24Reclame Aqui4012508barbara.priscila@gympass.comflaviaduarte@vooal.com1188144671.03.0NaN1333.06891.0NaN14.00.2500000.000
10293219066870.02021-01-07Reclame Aqui3489502suellen.franco+core@gympass.comlucashaddadf@gmail.com1190668701.01.0NaN1717.02931.0NaN38.00.0000000.000